library(Seurat)
library(ggplot2)
library(cowplot)

cdS1 <- readRDS("~/Documents/Experiments1/scRNAseq/Cerebellum_publication/allCells/data/Seurat3.rds")
cdS1@meta.data$dataset <- "E13"
colnames(cdS1@meta.data)[1:2] <- c("nCount_RNA","nFeature_RNA")

cdS2 <- readRDS("./E14/Seurat_E14.rds")
cdS3 <- readRDS("./E15/Seurat_E15.rds")
cdS4 <- readRDS("./E16/Seurat_E16.rds")

cdS1@meta.data$dataset1 <- "E13"
cdS2@meta.data$dataset1 <- "E14"
cdS3@meta.data$dataset1 <- "E15"
cdS4@meta.data$dataset1 <- "E16"

cdS <- merge(cdS1, c(cdS2, cdS3, cdS4))
cb.list <- SplitObject(object = cdS, split.by = "dataset1")

for (i in 1:length(x = cb.list)) {
  cb.list[[i]] <- NormalizeData(object = cb.list[[i]], verbose = FALSE)
  cb.list[[i]] <- FindVariableFeatures(object = cb.list[[i]], 
                                             selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}

anchors <- FindIntegrationAnchors(object.list = cb.list, dims = 1:30)
cdS.i <- IntegrateData(anchorset = anchors, dims = 1:30)

DefaultAssay(object = cdS.i) <- "integrated"

cdS.i <- ScaleData(object = cdS.i, verbose = FALSE)
cdS.i <- RunPCA(object = cdS.i, npcs = 40, verbose = FALSE)
ElbowPlot(cdS.i, ndims = 40, reduction = "pca")

cdS.i <- FindNeighbors(cdS.i, reduction = "pca", assay = "integrated", dims = 1:23)

Computing nearest neighbor graph
Computing SNN

cdS.i <- FindClusters(cdS.i, modularity.fxn = 1,
                    resolution = 1, algorithm = 1, n.start = 10, n.iter = 10,
                    random.seed = 0, temp.file.location = NULL, edge.file.name = NULL,
                    verbose = F)

myplots <- list()
for (n in 1:length(res)){
  res1 <- paste("integrated_snn_res",res[n],sep=".")
  Idents(object = cdS.i) <- cdS.i@meta.data[,res1]
  myplots[[n]] <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T)+
    NoAxes()+NoLegend()
}
myplots1 <- list()
for (n in 1:length(res)){
  res1 <- paste("integrated_snn_res",res[n],sep=".")
  Idents(object = cdS.i) <- cdS.i@meta.data[,res1]
  myplots1[[n]] <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
    NoAxes()+NoLegend()
}
gridExtra::grid.arrange(grobs= myplots, ncol = 2, top = paste0("resolutions: ", res))

gridExtra::grid.arrange(grobs= myplots1, ncol = 2, top = paste0("resolutions: ", res))

res1 <- paste("integrated_snn_res",2,sep=".")
Idents(object = cdS.i) <- cdS.i@meta.data[,res1]

cdS.i <- RunUMAP(object = cdS.i, dims = 1:23, reduction = "pca",
                      features = NULL, assay = "integrated", nneighbors = 65L, max.dim = 2L,
                      min.dist = 0.4, reduction.name = "umap", reduction.key = "UMAP_",
                      metric = "correlation", seed.use = 42)
DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)

DimPlot(cdS.i, reduction = "umap", group.by = "cellType", label=T, split.by = "dataset1")+
  NoAxes()+NoLegend()

DimPlot(cdS.i, reduction = "tsne", group.by = "cellType", label=T, split.by = "dataset1")+
  NoAxes()+NoLegend()

p1 <- DimPlot(cdS.i, reduction = "tsne", group.by = "cellType", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()
p2 = DimPlot(cdS.i, reduction = "umap", group.by = "cellType", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()
p3 <- DimPlot(cdS.i, reduction = "tsne", group.by = "dataset1", label=F)+
  NoAxes()
p4 <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p5 = cowplot::plot_grid(p3,p4, rel_widths = c(1,0.8))
p6 <- DimPlot(cdS.i, reduction = "umap", group.by = "dataset1", label=F)+
  NoAxes()
p7 <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p8 = cowplot::plot_grid(p6,p7, rel_widths = c(1,0.8))

pdf("summary.pdf", h=15, w=10)
cowplot::plot_grid(p5,p1, ncol =1, rel_heights = c(0.9,2))
cowplot::plot_grid(p8,p2, ncol =1, rel_heights = c(0.9,2))

dev.off()

null device 
          1 

p1 <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T) +
  NoAxes()+NoLegend()
p2 <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p3 <- DimPlot(cdS.i, reduction = "umap", group.by = "dataset1", label=F)
p4 <- DimPlot(cdS.i, reduction = "umap", group.by = "cellType", label=T)+
  NoAxes()+NoLegend()
cowplot::plot_grid(p1,p2,p3,p4)

p5 <- DimPlot(cdS.i, reduction = "tsne", group.by = "cellType", repel = T, label=T, split.by = "dataset1") +
  NoAxes()+NoLegend()
p6 <- DimPlot(cdS.i, reduction = "umap", group.by = "cellType", repel = T, label=T, split.by = "dataset1")+
  NoAxes()+NoLegend()
cowplot::plot_grid(p5,p6, ncol =1)

saveRDS(file = "Seurat_merge.rds",cdS.i)

mGenes <- readRDS(file="/Volumes/jali/Genome/Annotation1.rds")
sig_genes <- FindAllMarkers(cdS.i, features = NULL,
                            logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
                            min.diff.pct = -Inf, verbose = F, only.pos = TRUE,
                            max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL,
                            min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1,
                            return.thresh = 0.01)

sig_genes <- mutate(sig_genes,pct.diff=pct.1-pct.2)

sig_genes$TF <- "no"
id <- intersect(mGenes$mgi_symbol,sig_genes$gene)

x <- sig_genes[sig_genes$gene %in% id,]
sig_genes$TF[sig_genes$gene %in% id] <- mGenes$Type[match(x$gene,mGenes$mgi_symbol)]
sig_genes$description <- mGenes$description[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
head(sig_genes)

sig_genes <- sig_genes[, c("gene","TF","description","p_val","p_val_adj","avg_logFC","pct.1","pct.2","pct.diff","cluster")]
openxlsx::write.xlsx(sig_genes,"sigGenes.xlsx")

top <- sig_genes %>% group_by(cluster) %>% top_n(5, avg_logFC);top
tab <- as.data.frame(table(Idents(object = cdS.i)))
top$cellNumber <- tab$Freq[match(top$cluster,tab$Var1)]
top$percentage <- round(top$cellNumber/ncol(cdS.i)*100, digits = 2)
openxlsx::write.xlsx(top,"sigGenes_top5.xlsx")

top5 <- openxlsx::read.xlsx("sigGenes_sum.xlsx", sheet =2)
top5a <- top5[!duplicated(top5$cluster),]
top5a$cellType

 [1] "PC.Etv1"   "PC.Foxp1"  "PC.Nrgn"   "PC.En1"    "GABA.Pre"  "NPC.S"     "EGL.S"     "BG"        "EGL.S1"    "EGL"       "GC1"       "GABA.Pre1" "C1"        "IN"       
[15] "EGL.Pvalb" "GC2"       "EGL.M"     "NPC.M"     "Tlx3"      "BG"        "PC.Cck"    "GABA.Pro"  "IN"        "lCN"       "GABA.Pro"  "EGL.M1"    "mCN"       "NPC.M"    
[29] "lCN?"      "PC.Cdh9"   "AS"        "AS"        "NPC"       "Peri"      "Endo"      "MidO"      "Micro"     "Eryth"     "Isl1"     

cdS.i@meta.data$cellType1 <- plyr::mapvalues(x = Idents(cdS.i), from = top5a$cluster, to = top5a$cellType)
DimPlot(cdS.i, reduction = "tsne", group.by = "cellType1", label=T, repel = T)+
  NoLegend()

DimPlot(cdS.i, reduction = "umap", group.by = "cellType1", label=T, repel = T)+
  NoLegend()

table(cdS.i@meta.data$cellType1, cdS.i@meta.data$dataset1)

           
            E13 E14 E15 E16
  PC.Etv1   955 482 323  52
  PC.Foxp1  830 488 314  68
  PC.Nrgn   747 482 294  72
  PC.En1    823 326 244  51
  GABA.Pre  542 477 295 113
  NPC.S     457 306 283 173
  EGL.S     197 159 396 440
  BG        480 499 484 342
  EGL.S1    163 136 331 432
  EGL       133 139 365 420
  GC1       153 165 310 395
  GABA.Pre1 311 324 224 111
  C1        306 164 281  91
  IN        319 347 436 298
  EGL.Pvalb 152 150 238 240
  GC2       168  48 189 335
  EGL.M     120  97 232 271
  NPC.M     438 306 277 159
  Tlx3      171 338 140  22
  PC.Cck    298 150 145  20
  GABA.Pro  345 344 277 172
  lCN       138 271  94  32
  EGL.M1     84  58 167 214
  mCN       180 101 111 120
  lCN?      103 181  93  31
  PC.Cdh9   111  90 144  35
  AS        172 187 224 154
  NPC       254  41  48   4
  Peri       41  80  97 109
  Endo       36  63  90  48
  MidO       25  94  85  31
  Micro       4  37  57  54
  Eryth      37   7  22  55
  Isl1       13  57  25   7

p1 <- DimPlot(cdS.i, reduction = "tsne", group.by = "cellType1", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()
p2 = DimPlot(cdS.i, reduction = "umap", group.by = "cellType1", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()
p3 <- DimPlot(cdS.i, reduction = "tsne", group.by = "dataset1", label=F)+
  NoAxes()
p4 <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p5 = cowplot::plot_grid(p3,p4, rel_widths = c(1,0.8))
p6 <- DimPlot(cdS.i, reduction = "umap", group.by = "dataset1", label=F)+
  NoAxes()
p7 <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p8 = cowplot::plot_grid(p6,p7, rel_widths = c(1,0.8))

pdf("summary1.pdf", h=15, w=10)
cowplot::plot_grid(p5,p1, ncol =1, rel_heights = c(0.9,2))
cowplot::plot_grid(p8,p2, ncol =1, rel_heights = c(0.9,2))

dev.off()

null device 
          1 

sessionInfo()

R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] bindrcpp_0.2.2     dplyr_0.7.8        cowplot_0.9.2      ggplot2_3.1.0.9000 Seurat_3.0.0.9000 

loaded via a namespace (and not attached):
 [1] httr_1.3.1         tidyr_0.8.2        jsonlite_1.5       viridisLite_0.3.0  splines_3.4.3      R.utils_2.6.0      gtools_3.5.0       assertthat_0.2.0  
 [9] yaml_2.1.19        ggrepel_0.8.0      globals_0.12.4     backports_1.1.2    pillar_1.2.2       lattice_0.20-35    glue_1.3.0         reticulate_1.8    
[17] digest_0.6.18      RColorBrewer_1.1-2 SDMTools_1.1-221   colorspace_1.3-2   htmltools_0.3.6    Matrix_1.2-14      R.oo_1.22.0        plyr_1.8.4        
[25] pkgconfig_2.0.2    tsne_0.1-3         listenv_0.7.0      purrr_0.2.5        scales_1.0.0.9000  RANN_2.5.1         gdata_2.18.0       openxlsx_4.0.17   
[33] Rtsne_0.13         tibble_1.4.2       withr_2.1.2        ROCR_1.0-7         pbapply_1.3-4      lazyeval_0.2.1     survival_2.42-3    magrittr_1.5      
[41] evaluate_0.10.1    R.methodsS3_1.7.1  future_1.10.0      MASS_7.3-50        gplots_3.0.1       ica_1.0-2          tools_3.4.3        fitdistrplus_1.0-9
[49] data.table_1.11.8  stringr_1.3.0      plotly_4.7.1       munsell_0.5.0      cluster_2.0.7-1    irlba_2.3.2        compiler_3.4.3     rsvd_0.9          
[57] caTools_1.17.1     rlang_0.3.0.9000   grid_3.4.3         ggridges_0.5.0     htmlwidgets_1.2    igraph_1.2.1       base64enc_0.1-3    rmarkdown_1.9     
[65] labeling_0.3       bitops_1.0-6       gtable_0.2.0       codetools_0.2-15   R6_2.3.0           gridExtra_2.3      zoo_1.8-1          knitr_1.20        
[73] future.apply_1.0.1 rprojroot_1.3-2    bindr_0.1.1        KernSmooth_2.23-15 metap_0.9          stringi_1.2.4      parallel_3.4.3     Rcpp_1.0.0        
[81] png_0.1-7          tidyselect_0.2.5   lmtest_0.9-36     

---
title: "R Notebook"
output: html_notebook
---

# Load packages
```{r load-packages, message=F, warning=F}
library(Seurat)
library(ggplot2)
library(cowplot)
```


# Load previously saved datasets and add annotation
```{r}
cdS1 <- readRDS("~/Documents/Experiments1/scRNAseq/Cerebellum_publication/allCells/data/Seurat3.rds")
cdS1@meta.data$dataset <- "E13"
colnames(cdS1@meta.data)[1:2] <- c("nCount_RNA","nFeature_RNA")

cdS2 <- readRDS("./E14/Seurat_E14.rds")
cdS3 <- readRDS("./E15/Seurat_E15.rds")
cdS4 <- readRDS("./E16/Seurat_E16.rds")

cdS1@meta.data$dataset1 <- "E13"
cdS2@meta.data$dataset1 <- "E14"
cdS3@meta.data$dataset1 <- "E15"
cdS4@meta.data$dataset1 <- "E16"
```


# Dataset preprocessing
```{r}
cdS <- merge(cdS1, c(cdS2, cdS3, cdS4))
cb.list <- SplitObject(object = cdS, split.by = "dataset1")
```

# Normalize data and find variable genes
```{r}
for (i in 1:length(x = cb.list)) {
  cb.list[[i]] <- NormalizeData(object = cb.list[[i]], verbose = FALSE)
  cb.list[[i]] <- FindVariableFeatures(object = cb.list[[i]], 
                                             selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}
```

# Integrate the deatasets
```{r}
anchors <- FindIntegrationAnchors(object.list = cb.list, dims = 1:30)
cdS.i <- IntegrateData(anchorset = anchors, dims = 1:30)
```

# switch to integrated assay
```{r}
DefaultAssay(object = cdS.i) <- "integrated"
```

# Run the standard workflow for visualization and clustering
```{r}
cdS.i <- ScaleData(object = cdS.i, verbose = FALSE)
cdS.i <- RunPCA(object = cdS.i, npcs = 40, verbose = FALSE)
ElbowPlot(cdS.i, ndims = 40, reduction = "pca")
```

# Find clusters
```{r}
cdS.i <- FindNeighbors(cdS.i, reduction = "pca", assay = "integrated", dims = 1:23)
```


```{r}
res <- seq(0.5,4.0,0.5)
cdS.i <- FindClusters(cdS.i, modularity.fxn = 1,
                    resolution = res, algorithm = 1, n.start = 10, n.iter = 10,
                    random.seed = 0, temp.file.location = NULL, edge.file.name = NULL,
                    verbose = F)
```

# Inspect cell clusters
```{r fig.width=4, fig.asp=2}
myplots <- list()
for (n in 1:length(res)){
  res1 <- paste("integrated_snn_res",res[n],sep=".")
  Idents(object = cdS.i) <- cdS.i@meta.data[,res1]
  myplots[[n]] <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T)+
    NoAxes()+NoLegend()
}
myplots1 <- list()
for (n in 1:length(res)){
  res1 <- paste("integrated_snn_res",res[n],sep=".")
  Idents(object = cdS.i) <- cdS.i@meta.data[,res1]
  myplots1[[n]] <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
    NoAxes()+NoLegend()
}

gridExtra::grid.arrange(grobs= myplots, ncol = 2, top = paste0("resolutions: ", res))
gridExtra::grid.arrange(grobs= myplots1, ncol = 2, top = paste0("resolutions: ", res))
```

```{r}
res1 <- paste("integrated_snn_res",2,sep=".")
Idents(object = cdS.i) <- cdS.i@meta.data[,res1]
```


# Visualize the results with t-SNE
```{r}
cdS.i <- RunTSNE(cdS.i, reduction = "pca", cells = NULL,
               dims = 1:23, features = NULL, seed.use = 1, tsne.method = "Rtsne",
               add.iter = 0, dim.embed = 2, distance.matrix = NULL,
               reduction.name = "tsne", reduction.key = "tSNE_", perplexity = 50)
DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T)
```

# Visualize the results with UMAP
```{r}
cdS.i <- RunUMAP(object = cdS.i, dims = 1:23, reduction = "pca",
                      features = NULL, assay = "integrated", nneighbors = 65L, max.dim = 2L,
                      min.dist = 0.4, reduction.name = "umap", reduction.key = "UMAP_",
                      metric = "correlation", seed.use = 42)
DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)
```

# Visualize the results with UMAP
```{r}
cdS.i <- RunUMAP(object = cdS.i, dims = 1:23, reduction = "pca",
                      features = NULL, assay = "integrated", nneighbors = 65L, max.dim = 2L,
                      min.dist = 0.4, reduction.name = "umap", reduction.key = "UMAP_",
                      metric = "correlation", seed.use = 42)
DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)
```

```{r fig.asp=1}
DimPlot(cdS.i, reduction = "umap", group.by = "cellType", label=T, split.by = "dataset1")+
  NoAxes()+NoLegend()
```

```{r fig.asp=1}
DimPlot(cdS.i, reduction = "tsne", group.by = "cellType", label=T, split.by = "dataset1")+
  NoAxes()+NoLegend()
```

```{r}
p1 <- DimPlot(cdS.i, reduction = "tsne", group.by = "cellType", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()
p2 = DimPlot(cdS.i, reduction = "umap", group.by = "cellType", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()



p3 <- DimPlot(cdS.i, reduction = "tsne", group.by = "dataset1", label=F)+
  NoAxes()
p4 <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p5 = cowplot::plot_grid(p3,p4, rel_widths = c(1,0.8))

p6 <- DimPlot(cdS.i, reduction = "umap", group.by = "dataset1", label=F)+
  NoAxes()
p7 <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p8 = cowplot::plot_grid(p6,p7, rel_widths = c(1,0.8))

```

```{r}
pdf("summary.pdf", h=15, w=10)
cowplot::plot_grid(p5,p1, ncol =1, rel_heights = c(0.9,2))
cowplot::plot_grid(p8,p2, ncol =1, rel_heights = c(0.9,2))
dev.off()
```



```{r}
p1 <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T) +
  NoAxes()+NoLegend()
p2 <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p3 <- DimPlot(cdS.i, reduction = "umap", group.by = "dataset1", label=F)
p4 <- DimPlot(cdS.i, reduction = "umap", group.by = "cellType", label=T)+
  NoAxes()+NoLegend()

cowplot::plot_grid(p1,p2,p3,p4)
```



```{r fig.width=8, fig.asp=1.5}
p5 <- DimPlot(cdS.i, reduction = "tsne", group.by = "cellType", repel = T, label=T, split.by = "dataset1") +
  NoAxes()+NoLegend()
p6 <- DimPlot(cdS.i, reduction = "umap", group.by = "cellType", repel = T, label=T, split.by = "dataset1")+
  NoAxes()+NoLegend()
cowplot::plot_grid(p5,p6, ncol =1)
```

# Save the Seurat object
```{r}
saveRDS(file = "Seurat_merge.rds",cdS.i)
```


```{r}
mGenes <- readRDS(file="/Volumes/jali/Genome/Annotation1.rds")
sig_genes <- FindAllMarkers(cdS.i, features = NULL,
                            logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
                            min.diff.pct = -Inf, verbose = F, only.pos = TRUE,
                            max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL,
                            min.cells.feature = 3, min.cells.group = 3, pseudocount.use = 1,
                            return.thresh = 0.01)

sig_genes <- mutate(sig_genes,pct.diff=pct.1-pct.2)

sig_genes$TF <- "no"
id <- intersect(mGenes$mgi_symbol,sig_genes$gene)

x <- sig_genes[sig_genes$gene %in% id,]
sig_genes$TF[sig_genes$gene %in% id] <- mGenes$Type[match(x$gene,mGenes$mgi_symbol)]
sig_genes$description <- mGenes$description[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
head(sig_genes)

sig_genes <- sig_genes[, c("gene","TF","description","p_val","p_val_adj","avg_logFC","pct.1","pct.2","pct.diff","cluster")]
openxlsx::write.xlsx(sig_genes,"sigGenes.xlsx")

top <- sig_genes %>% group_by(cluster) %>% top_n(5, avg_logFC);top
tab <- as.data.frame(table(Idents(object = cdS.i)))
top$cellNumber <- tab$Freq[match(top$cluster,tab$Var1)]
top$percentage <- round(top$cellNumber/ncol(cdS.i)*100, digits = 2)
openxlsx::write.xlsx(top,"sigGenes_top5.xlsx")
```

# Annotate cell cluster
Annotate the cell culster in Excel.
```{r fig.width=5, fig.asp=1}
top5 <- openxlsx::read.xlsx("sigGenes_sum.xlsx", sheet =2)
top5a <- top5[!duplicated(top5$cluster),]
top5a$cellType

cdS.i@meta.data$cellType1 <- plyr::mapvalues(x = Idents(cdS.i), from = top5a$cluster, to = top5a$cellType)
DimPlot(cdS.i, reduction = "tsne", group.by = "cellType1", label=T, repel = T)+
  NoLegend()
DimPlot(cdS.i, reduction = "umap", group.by = "cellType1", label=T, repel = T)+
  NoLegend()

```

```{r}
table(cdS.i@meta.data$cellType1, cdS.i@meta.data$dataset1)
```


```{r}
p1 <- DimPlot(cdS.i, reduction = "tsne", group.by = "cellType1", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()
p2 = DimPlot(cdS.i, reduction = "umap", group.by = "cellType1", label=T, split.by = "dataset1", repel = T)+
  NoAxes()+NoLegend()

p3 <- DimPlot(cdS.i, reduction = "tsne", group.by = "dataset1", label=F)+
  NoAxes()
p4 <- DimPlot(cdS.i, reduction = "tsne", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p5 = cowplot::plot_grid(p3,p4, rel_widths = c(1,0.8))

p6 <- DimPlot(cdS.i, reduction = "umap", group.by = "dataset1", label=F)+
  NoAxes()
p7 <- DimPlot(cdS.i, reduction = "umap", group.by = "ident", label=T)+
  NoAxes()+NoLegend()
p8 = cowplot::plot_grid(p6,p7, rel_widths = c(1,0.8))

```

```{r}
pdf("summary1.pdf", h=15, w=10)
cowplot::plot_grid(p5,p1, ncol =1, rel_heights = c(0.9,2))
cowplot::plot_grid(p8,p2, ncol =1, rel_heights = c(0.9,2))
dev.off()
```

# Print sesstion information
```{r}
sessionInfo()
```

